ASMase Activation in Ultrasound-Stimulated Radiation Enhancement Using MRI-Guided Focused Ultrasound
Bibliographic record
Abstract
Focused ultrasound-stimulated microbubble (MB + FUS) therapy is a promising radiation enhancement strategy, utilizing vascular disruption to enhance radiation efficacy. However, its mechanistic effects in large tumour volumes and clinical translatability remain insufficiently characterized. This study evaluates the synergistic impact of MB + FUS combined with radiation therapy (XRT) in a large-scale, immunosuppressed rabbit xenograft model using a clinically adaptable, MRI-guided 6144-element focused ultrasound (MRgFUS) system. Tumours were treated with MB + FUS, XRT, or both, with real-time image-guided MB activation and evaluation of treatment effects on vascular integrity, proliferation, and cellular stress responses. Assessments included Power Doppler ultrasound, histology, and immunohistochemistry targeting TUNEL, ASMase, Ki-67, Factor VIII, HIF-1α, and VEGF. Combination therapy induced significant vascular collapse, reduced perfusion, and decreased Factor VIII expression, alongside increased nuclear condensation, TUNEL positivity, and ASMase expression, consistent with ASMase-mediated endothelial apoptosis and vascular disruption. Upregulation of HIF-1α and VEGF indicated hypoxia-driven angiogenic signalling, while Ki-67 suppression reflected sustained tumour growth inhibition. Although immune responses were limited by host immunosuppression, the larger tumour burden provided clinically relevant constraints. The MRgFUS platform enabled precise and reproducible MB activation, reinforcing MB + FUS as a potent radio-enhancement modality. These findings support the continued development of MB + FUS toward clinical translation and highlight its potential as a complementary strategy to radiation therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".